This highlights that the method used to define θfc in our study, while objective and tied strictly to soil moisture retention parameters, produced θfc estimates that are relatively conservative from a flow-based definition of θfc, as they are based on how a 1-cm slice of soil would drain. In a soil profile that has been deeply wetted, such as those used in this Ag-MAR modeling study, the defined θfc cannot be achieved by drainage alone within a reasonable time-frame, even at 10-cm depth in a 200-cm sandy loam profile . Thus, the corresponding time-to trafficability estimates should be interpreted as relatively conservative, especially for those soils with low plasticity indices such as sands and sandy loams. This is not to say, however, that the definitions used in this study are outside the norms of soil science. θfc is often defined with a standard tension . All textures but silt loam have estimated θfc values that correspond to this tension range . Finer-textured soils may still have some risk of compaction at the thresholds defined in this study, given their high plasticity indices and the relatively high Ksat estimates produced by the ROSETTA pedotransfer function for these textures . Similarly, while presence of a Bt horizon underlying various surface textures did not consistently delay time-to-trafficability, ROSETTA may overestimate the permeability of 2:1 clay enriched sub-soils occurring on, for example, stable river terraces above current floodplains,hydroponic container system especially in the eastern uplands of the San Joaquin Valley . Thus, these landscapes should be treated more cautiously if used for Ag-MAR during periods when trafficability is required, especially during low PET conditions.
For example, in an Australian study of Vertisol trafficability under irrigated cotton production, researchers concluded that risk-free trafficability only really existed at water contents near wilting point , which is equivalent to about 60% of the mean θfc for clays in our study . On the other hand, this contrasts sharply with a field study in the Netherlands which found that a heavy clay soil under pasture was trafficable at just 90 cm soil moisture tension based on observation of compaction patterns and tensiometer readings , which is moister than the wettest, commonly used tension-based definition of θfc . An additional uncertainty in trafficability and work ability research is the extent to which surficial trafficability and work ability moisture thresholds are sufficient to prevent detrimental subsoil compaction that requires more effort to ameliorate . Field validation studies that include modeling of soil moisture to predict suitable days for agricultural operations and that simultaneously examine full soil profile effects of wheel traffic occurring at or below these moisture thresholds have not yet been reported. A study of controlled traffic farming systems in California cotton production highlights this need, since bulk density increased to 25-cm depth while penetrometer resistance increased to at least 100-cm soil depth under wheel traffic in a sandy loam soil , but no operational decisions were guided by trafficability soil moisture thresholds in their study. Finally, the time-to-trafficability estimates are meant to guide operational decisions when crops are dormant or fields are fallow, given that root water uptake was intentionally neglected and only drainage and bare soil evaporation were considered in H1D simulations. For major perennial crops, end of dormancy typically ranges from mid February to late-April , spanning the time period addressed by this study. There are several reasons for omitting scenarios when root water uptake is active. First, Ag-MAR is recognized to be a risk to many actively growing crops due to the possibility of developing anoxic soil conditions . Second, for deep wetting events more generally, accurate root water uptake modeling requires knowledge of root depth distribution and crop canopy coverage. Third, the need for irrigation water may arise before the soil moisture trafficability threshold during active root water uptake for more sensitive crops or during specific periods of growth.
All of these considerations complicate the ability to provide a generalizable time-to-trafficability tool to growers that also accounts for crop root water uptake.A relationship between global warming and increased concentrations of greenhouse gases such as carbon dioxide , produced by the burning of fossil fuels, is suggested by much accumulating evidence. As far back as 1992, more than 150 governments attending the Rio Earth Summit signed the Framework Convention on Global Climate change. Article 2 states that the ”ultimate objective of this Convention … is to achieve … stabilization of greenhouse gas concentrations that would prevent dangerous anthropogenic interference with the climate system.” More than ten years later, the questions remain: how ”dangerous” are the consequences of anthropogenic interference, and how much ”stabilization” is justified? The economics literature so far has given mixed results with regards to the impact on agriculture.1 In the remainder of this section we give a brief overview of previous approaches to set the stage for our study. These can be divided into three broad categories, beginning with the agronomic approach, based on the use of agronomic models that simulate crop growth over the life cycle of the plant and measure the effect of changed climate conditions on crop yield and input requirements. For example, Adams relies on crop simulation models to derive the predicted change for both irrigated and rainfed wheat, corn, and soybeans. The predicted changes in yields are then combined with economic models of farm level crop choice, using linear or nonlinear programming.The analysis, however, usually considers variable but not fixed costs of production. It often turns out to be necessary to add artificial constraints to make the programming model solution replicate actual farmer behavior in the baseline period. Moreover, the analysis focuses on the agricultural sector, and ignores the linkages with the remainder of the economy which would make the input prices and input allocations to agriculture endogenous.
This is remedied in the computable general equilibriumapproach, which models agriculture in relation to the other major sectors of the economy and allows resources to move between sectors in response to economic incentives. An example is FARM, the eight-region CGE model of the world agricultural economy by the United State Department of Agriculture. However, while a CGE model has the advantages of making prices endogenous and accounting for inter-sectoral linkages, these come at the cost of quite drastic aggregation in which spatially and economically diverse sectors are characterized by a representative farm or firm. In summary, on the one hand the agronomic models do not fully capture the adaptation and mitigation strategies of farmers in the face of climate change, while on the other the CGE models are only appropriate to highly aggregated sectors of the economy. Mendelsohn, Nordhaus and Shaw provide an interesting middle ground, proposing what they call a Ricardian approach, essentially a hedonic model of farmland pricing, based on the notion that the value of a tract of land capitalizes the discounted value of all future profits or rents that can be derived from the land. The advantage of the hedonic approach is that it relies on the cross-sectional variation to identify the implicit choices of landowners regarding the allocation of their land among competing uses instead of directly modeling their decision. Further,planter pots drainage the hedonic function also allows one to calculate the direct impact on each farmer, county or state, in contrast to the highly aggregated structural CGE models. This is the approach we adopt, though with a number of innovations indicated below and explained in detail in succeeding sections. In this paper we resolve some of the differences in previous studies by estimating a hedonic equation for farmland value east of the 100th meridian, the boundary of the region in the United States where farming is possible without irrigation. The main contributions of the paper are: First, we incorporate climate differently than previous studies, by using transformations of the climatic variables suggested by the agronomic literature. The relationship between climatic variables and plant growth is highly nonlinear and our approach yields results that are consistent with the agronomic evidence. Second, we develop a new data set that integrates the spatial distribution of soil and climatic variables within a county with the help of a Landsat satellite scan of the contiguous United States. Third, we allow the error terms to be spatially correlated to obtain a more efficient estimator and correct t-values . Fourth, we present several sensitivity checks, and show that our results are robust to both different specifications and census years. We show that results remain similarly unchanged when we include state fixed effects to control for the influence of state-specific factors unrelated to climate, such as property taxes and crop subsidies. Finally, we evaluate potential impacts of warming using new climate projections from the most recent runs of two of the major global climate models. The paper is organized as follows. Section 2 outlines a model of farmland value with attention to issues raised by irrigation. Section 3 addresses spatial issues that arise in the definition and measurement of climatic and soil variables and in the correlation of error terms.
Section 4 presents our empirical results, including tests for spatial correlation and estimates of the hedonic regression coefficients and discusses a variety of tests of robustness of the results. Section 5 uses the results to generate estimates of regionally differentiated impacts of climate change on agriculture. Section 6 summarizes our conclusions. In this framework, climate variables play two different roles. Temperature is an exogenous shift variable in the production function; increases in temperature increase the demand for water as an input and they can raise or lower yield, depending on the size of the increase.Precipitation has a different role in irrigated areas than in dryland areas. In dryland areas, the water supply for crops comes from precipitation falling on the field before and during the growing season; in this case, the water supply is fixed by nature in any given year, and it comes with a price of zero. In irrigated areas, by contrast, the water supply is man-made, using local groundwater or surface water imported from somewhere else, it comes at a cost, and the quantity is endogenously determined. In terms of location, since the time of John Wesley Powell it has been common to take the 100th meridian as a rough approximation of the rainfall line in the US. To the east, rainfall generally exceeds 20 inches per year while, to the west, rainfall is generally less than 20 inches per year. Since virtually all traditional US crops require at least 20 inches of water to grow, the 100th meridian marks the boundary of the arid West, where farming is generally possible only with use of irrigation.3 Thus the 17 western states account for about 88% of the 150 million acre feet of irrigation water used annually in the U.S. The economic implications of the distinction between dryland and irrigated farming are discussed in detail by Cline , Darwin , and Schlenker et al. , and will be summarized briefly here. In addition to the fact that precipitation does not measure water supply in the arid West, the other distinctive feature is that, in irrigated areas, future changes in water costs, unlike other input costs, are not likely to be capitalized in future land prices in the same way as past cost changes were capitalized in past land values. Many of the major surface water supply projects in the western United States were developed by the US Bureau of Reclamation or the Army Corps of Engineers and involved a substantial subsidy to farmers. Depending on the age of the project, there is substantial variation in federal irrigation charges across different projects, and these are clearly capitalized into farmland values. Failure to account for subsidies could bias other regression coefficients, especially climatic coefficients that in turn are correlated with the access to irrigation. Aside from the federal projects, the remainder of the irrigation supply in the western states comes from groundwater or from non-federal surface water storage projects.Nevertheless, in the case of irrigation with non-federal surface water it still would be misleading to predict the economic cost of a change in precipitation on the basis of a hedonic regression of current farmland values.